The aim of this research is to examine the conditions under which education is more or less beneficial to health. The association between education and health is well-established, but whether the strength of this association depends on other social statuses is not. We propose to develop and test a theory of "resource substitution" which states that education's influence on health is greater for person's with fewer alternative resources than it is for the more advantaged. We focus mainly on three disadvantaged socioeconomic statuses, representing disadvantaged social origins (low parental education), individual socioeconomic disadvantage (low income) and neighborhood disadvantage (indexed by the prevalence of poverty, mother-only households, college education, and home ownership). The first precedes ones own educational attainment, and the other two follow from it. We also examine a number of other disadvantaged statuses that could modify the effect of education on health, including parental divorce, female gender, minority status, lack of full-time employment, and perceived neighborhood disorder. We will compare the hypotheses derived from resource substitution theory with the alternative (which we call "resource multiplication") which suggests that the influence of education is health is greater for persons with more resources. Finally we will examine mediators of the moderators, ff education has a larger effect on health for the disadvantaged, why does it? We will analyze data from our two longitudinal surveys, "Aging, Status, and the Sense of Control" (ASOC; U.S. sample) and "Community, Crime, and Health" (CCH; Illinois sample). We measured self-reported health, physical functioning, chronic conditions, and psychological symptoms; the potential mediators and moderators of the education-health association; detailed information about education; and, in CCH, linked census data on respondents' neighborhoods. We will model the changes in health with subsequent and concurrent change models, structural equation models with latent factors, self-amplifying latent curve models, and multilevel models.